Machine translation using abductive inference
Jerry R. Hobbs, Megumi Kameyama · 1991
Machine Translation and World Knowledge. Many existing approaches to machine translation take for granted that the information presented in the output is found somewhere in the input, and, moreover, that such information should be expressed at a single representational level, say, in terms of the parse trees or of "semantic" assertions. Languages, however, not only express the equivalent information by drastically different linguistic means, but also often disagree in what distinctions should be expressed linguistically at all. For example, in translating from Japanese to English, it is often necessary to supply determiners for noun phrases, and this in general cannot be done without deep understanding of the source text. Similarly, in translating from English to Japanese, politeness considerations, which in English are implicit in the social situation and explicit in very diffuse ways in, for example, the heavy use of hypotheticals, must be realized grammatically in Japanese. Machine translation therefore requires that the appropriate inferences be drawn and that the text be interpreted to some depth. Recently, an elegant approach to inference in discourse interpretation has been developed at a number of sites (e. g., Charniak and Goldman, 1988; Hobbs et al., 1990; Norvig, 1987), all based on the notion of abduction, and we have begun to explore its potential application to machine translation. We argue that this approach provides the possibility of deep reasoning and of mapping between the languages at a variety of levels.